Hypotension in the course of a Surgical Procedure can be Determined Using Machine Learning Algorithm

The algorithm was able to accurately predict an intraoperative hypotensive event 15 minutes before it occurred in 84% of cases, 10 minutes before in 84% of cases, and five minutes before in 87% of cases.

By detecting signs in routinely collected physiological data in surgical patients, a machine learning algorithm is able to predict potentially low blood pressure that may occur during surgical procedures.Hypotension or critical low blood pressure can lead to problems such as postoperative heart attack, acute kidney injury and even death. The algorithm was able to accurately predict an intraoperative hypotensive event 15 minutes before it occurred in 84% of cases, 10 minutes before in 84% of cases, and five minutes before in 87% of cases.

Researchers made use of two sets of data to develop and validate the predictive algorithm, based on recordings of the increase and decrease of blood pressure in the arteries during a heartbeat, including episodes of hypotension. For each heartbeat, they were able to derive 3,022 individual features from the arterial pressure waveforms, producing more than 2.6 million bits of information used to build the algorithm.

The authors concluded that the results showed that the machine learning algorithm can be trained, with large data sets of high-fidelity arterial waveforms, so as to identify hypotension in surgical patients’ records.

Maxime Cannesson, MD, vice chair for perioperative medicine and professor of anaesthesiology at UCLA Medical Center says it's the first time machine learning and computer science techniques have been applied to complex physiological signals obtained during surgery.

While future studies are needed to evaluate the real-time value of such algorithms in a broader set of clinical conditions and patients, Cannesson contends that the research “opens the door to the application of these techniques to many other physiological signals, such as EKG for cardiac arrhythmia prediction or EEG for brain function” and “could lead to a whole new field of investigation in clinical and physiological sciences and reshape our understanding of human physiology.”